The New Analytics Professional: Landing A Job In The Big Data Era

Along with the usual pomp and celebration of college commencements and high school graduation ceremonies we’re seeing now, the end of the school year also brings the usual brooding and questions about careers and next steps. Analytics is no exception, and with the big data surge continuing to fuel lots of analytics jobs and sub-specialties, the career questions keep coming. So here are a few answers on what it means to be an “analytics professional” today, whether you’re just entering the workforce, you’re already mid-career and looking to make a transition, or you need to hire people with this background.

The first thing to realize is that analytics is a broad term, and there are a lot of names and titles that have been used over the years that fall under the rubric of what “analytics professionals” do: The list includes “statistician,” “predictive modeler,” “analyst,” “data miner” and — most recently — “data scientist.” The term “data scientist” is probably the one with the most currency – and hype – surrounding it for today’s graduates and upwardly mobile analytics professionals. There’s even a backlash against over-use of the term by those who slap it loosely on resumes to boost salaries and perhaps exaggerate skills.

stairs_jobs

Labeling the Data Scientist

In reality, if you study what successful “data scientists” actually do and the skills they require to do it, it’s not much different from what other successful analytics professionals do and require. It is all about exploring data to uncover valuable insights often using very sophisticated techniques. Much like success in different sports depends on a lot of the same fundamental athletic abilities, so too does success with analytics depend on fundamental analytic skills. Great analytics professionals exist under many titles, but all share some core skills and traits.
The primary distinction I have seen in practice is that data scientists are more likely to come from a computer science background, to use Hadoop, and to code in languages like Python and R. Traditional analytics professionals, on the other hand, are more likely to come from a statistics, math or operations research background, are likely to work in relational or analytics server environments, and to code in SAS and SQL.

Regardless of the labels or tools of choice, however, success depends on much more than specific technical abilities or focus areas, and that’s why I prefer the term “data artist” to get at the intangibles like good judgment and boundless curiosity around data. I wrote an article on the data artist for the International Institute for Analytics (IIA). I also collaborated jointly with the IIA and Greta Roberts from Talent Analytics to survey a wide number of analytics professionals. One of our chief goals in that 2013 quantitative study was to find out whether analytics professionals have a unique, measurable mind-set and raw talent profile.

A Jack-of-All Trades

Our survey results showed that these professionals indeed have a clear, measurable raw talent fingerprint that is dominated by curiosity and creativity; these two ranked very high among 11 characteristics we measured. They are the qualities we should prioritize alongside the technical bona fides when looking to fill jobs with analytics professionals. These qualities also happen to transcend boundaries between traditional and newer definitions of what makes an analytics professional.

This is particularly true as we see more and more enterprise analytics solutions getting built from customized mixtures of multiple systems, analytic techniques, programming languages and data types. All analytics professionals need to be creative, curious and adaptable in this complex environment that lets data move to the right analytic engines, and brings the right analytic engines to where the data may already reside.
Given that the typical “data scientist” has some experience with Hadoop and unstructured data, we tend to ascribe the creativity and curiosity characteristics automatically (You need to be creative and curious to play in a sandbox of unstructured data, after all). But that’s an oversimplification, and our Talent Analytics/International Institute of Analytics survey shows that the artistry and creative mindset we need to see in our analytics professionals is an asset regardless of what tools and technologies they’ll be working with and regardless of what title they have on their business card. This is especially true when using the complex, hybrid “all-of-the-above” solutions that we’re seeing more of today and which Gartner IT -0.48% calls the Logical Data Warehouse.

Keep all this in mind as you move forward. The barriers between the worlds of old and new; open source and proprietary; structured and unstructured are breaking down. Top quality analytics is all about being creative and flexible with the connections between all these worlds and making everything work seamlessly. Regardless of where you are in that ecosystem or what kind of “analytics professional” you may be or may want to hire, you need to prioritize creativity, curiosity and flexibility – the “artistry” – of the job.

To read the original article on Forbes, click here.

Source by analyticsweekpick

Validating a Lostness Measure

No one likes getting lost. In real life or digitally.

One can get lost searching for a product to purchase, finding medical information, or clicking through a mobile app to post a social media status.

Each link, button, and menu leads to decisions. And each decision can result in a mistake, leading to wasted time, frustration, and often the inability to accomplish tasks.

But how do you measure when someone is lost? Is this captured already by standard usability metrics or is a more specialized metric needed? It helps to first think about how we measure usability.

Measuring Usability

We recommend a number of usability measures to assess the user experience, both objectively and subjectively (which come from the ISO 9241 standard of usability). Task completion, task time, and number of errors are the most common types of objective task-based measures. Errors take time to operationalize (“What is an error?”), while task completion and time can often be collected automatically (for example, in our MUIQ platform).

Perceived ease and confidence are two common task-based subjective measures—simply asking participants how easy or difficult or how confident they are they completed the task. Both tend to correlate (r ~ .5) with objective task measures [pdf]. But do any of these objective or subjective measures capture what it means to be lost?

What Does It Mean to Be Lost?

How do you know whether someone is lost? In real life you could simply ask them. But maybe people don’t want to admit they’re lost (you know, like us guys). Is there an objective way to determine lostness?

In the 1980s, as “hypertext” systems were being developed, a new dimension was added to information regarding behavior. Designers wanted to know whether people were getting lost when clicking all those links. Earlier, Elm and Woods (1985) argued that being lost was more than a feeling (no Boston pun intended); it was a degradation of performance that could be objectively measured. Inspired by this idea, in 1996 Patricia Smith sought to objectively define lostness and described a way to objectively measure when people were lost in hypertext. But not much has been done with it since (at least that we could find).

Smith’s work has received a bit of a resurgence after Tomer Sharon cited it in Validating Product Ideas and was consequently mentioned in online articles.

While there have been other methods for quantitatively assessing navigation, in this article we’ll take a closer look at how Smith quantified lostness and how the measure was validated.

A Lostness Measure

Smith proposed a few formulas to objectively assess lostness. The measure is essentially a function of what the user does (how many screens visited) relative to the most efficient path a user could take through a system. It requires first finding the minimum number of screens or steps it takes to accomplish a task—a happy path—and then comparing that to how many total screens and unique screens a user actually visits. She settled on the following formula using these three inputs to account for two dimensions of lostness:

N=Unique Pages Visited

S=Total Pages Visited

R=Minimum Number of Pages Required to Complete Task

The lostness measure ranges from 0 (absence of lostness) to 1 (being completely lost). Formulas can be confusing and they sometimes obscure what’s being represented, so I’ve attempted to visualize this metric and show how it’s derived with the Pythagorean theorem in Figure 1 below. 

Figure 1: Visualization of the lostness measure. The orange lines with the “C” is an example of how a score from one participant can be converted into lostness using the Pythagorean theorem.

Smith then looked to validate the lostness measure using data from a previous study using 20 students (16 and 17 year olds) from the UK. Participants were asked to look for information on a university department hypertext system. Measures collected included the total number of nodes (pages), deviations, and unique pages accessed.

After reviewing videos of the users across tasks, she found that her lostness measure did correspond to lost behavior. She identified the threshold of lostness scores above .5 as being lost, while scores below .4 as not lost, and the scores between .4 and .5 as indeterminate.

The measures were also used in another study that used eight participants with more nodes as reported in Smith. In the study by Cardle (a 1994 dissertation), similar findings of Lostness and Efficiency were found. But of the eight users, one had a score above .5 (indicating lost) when he was not really lost but exploring—suggesting a possible confound with the measure.

Replication Study

Given the small amount of data used to validate the lostness measure (and the dearth of information since), we conducted a new study to collect more data, to confirm thresholds of lostness, and see how this measure correlates with other widely used usability measures.

Between September and December 2018 we reviewed 73 videos of users attempting to complete 8 tasks from three studies. The studies included consumer banking websites, a mobile app for making purchases, and a findability task on the US Bank website that asked participants to find the name of the VP and General Counsel (an expected difficult task). Each task had a clear correct solution and “exploring” behavior wasn’t expected, thus minimizing possible confounds with natural browsing behavior that may look like lostness (e.g., looking at many pages repeatedly).

Sample sizes ranged from 5 to 16 for each task experience. We selected tasks that we hoped would provide a good range of lostness. For each task, we identified the minimum number of screens needed to complete each task for the lostness measure (R), and reviewed each video to count the total number of screens (S) and number of unique screens (N). We then computed the lostness score for each task experience. Post-task ease was collected using the SEQ and task time and completion rates were collected in the MUIQ platform.

Study Results

Across the 73 task experiences we had a good range of lostness, from a low of 0 (perfect navigation) to a high of .86 (very lost) and a mean lostness score of .34. We then aggregated the individual experiences by task.

Table 1 shows the lostness score, post-task ease, task time, and completion rate aggregated across the tasks, with lostness scores ranging from .16 to .72 (higher lostness scores mean more lostness).

Task Lostness Ease Time Completion % Lost
1 0.16 6.44 196 100% 6%
2 0.26 6.94 30 94% 19%
4 0.33 6.00 272 40% 40%
3 0.34 6.19 83 100% 44%
6 0.37 4.60 255 80% 60%
7 0.51 4.40 193 100% 40%
8 0.66 2.20 339 60% 100%
5 0.72 2.40 384 60% 100%

Table 1: Lostness, ease (7-point SEQ scale), time (in seconds), completion rates, and % lost (> .5) for the eight tasks. Tasks sorted by lostness score, from least lost to most lost.

Using the Smith “lost” threshold of .5, we computed a binary metric of lost/not lost for each video and computed the average percent lost per task (far right column in Table 1).

Tasks 8 and 5 have both the highest lostness scores and percent being lost. All participants had lostness scores above .5 and were considered “lost.” In contrast, only 6% and 19% of participants were “lost” on tasks 1 and 2.

You can see a pattern between lostness and the ease, time, and completion rates in Table 1. As users get more lost (lostness goes up), the perception of ease goes down, time goes up. The correlations between lostness and these task-level measures are shown in Table 2 at both the task level and individual level.

Metric Task Level
r
Individual Level
r
Ease -0.95* -0.52*
Comp -0.46 -0.17
Time 0.72* 0.51*

Table 2: Correlations between lostness and ease, completion rates, and time at the task level (n=8) and individual level (n = 73). * indicates statistically significant at the p < .05 level

As expected, correlations are higher at the task level as the individual variability is smoothed out through the aggregation, which helps reveal patterns. The correlation between ease and lostness is very high (r = -.95) at the task level and to a lesser extent at the individual level r = -.52. Interestingly, despite differing tasks, the correlation between lostness and task time is also high and significant at r= .72 and r = .51 at the task and individual levels.

The correlation with completion rate, while in the expected direction, is more modest and not statistically significant (see the “Comp” row in Table 2). This is likely a consequence of both the coarseness of this metric (binary) and a restriction in range with most tasks in our dataset having high completion rates.

The strong relation between perceived ease and lostness can be seen in the scatter plot in Figure 2, with users’ perception of the task ease accounting for a substantial ~90% of the variance in lostness. At least with our dataset, it appears that average lostness is well accounted for by ease. That is, participants generally rate high lostness tasks as difficult.

Figure 2: Relationship between lostness and ease (r = -.95) for the 8 tasks; p < .01. Dots represent the 8 tasks.

 

Ease N % Lost Mean Lostness
1 6 1.00 0.73
2 4 1.00 0.72
3 4 1.00 0.62
4 3 0.00 0.17
5 3 0.33 0.26
6 13 0.31 0.35
7 40 0.23 0.24

Table 3: Percent of participants “lost” and mean lostness score for each point on the Single Ease Question (SEQ).

Further examining the relationship between perceived ease and lostness, Table 3 shows the average percent of participants that were marked as lost (scores above .5) and the mean lostness score for each point on the Single Ease Question (SEQ) scale. More than half the task experiences were rated 7 (the easiest task score), which corresponds to low lostness scores (below .4). SEQ scores at 4 and below all have high lostness scores (above .6), providing an additional point of concurrent validity for the lostness measure. Table 4 further shows an interesting relationship. The threshold when lostness scores go from not lost to lost happens around the historical SEQ average score of 5.5, again suggesting that below average ease is associated with lostness. It also reinforces the idea that the SEQ (a subjective score) is a good concurrent indicator of behavior (objective data).

Lostness N Mean SEQ Score
0 28 6.6
0.3 12 6.3
0.4 4 6.5
0.5 6 5.7
0.6 5 4.4
0.7 8 3.5
0.8 10 4.1

Table 4: Lostness scores aggregated into deciles with corresponding mean SEQ scores at each decile.

Validating the Lostness Thresholds

To see how well the thresholds identified by Smith predicted actual lostness, we reviewed the videos again and made a judgment as to whether the user was struggling or giving any indication of lostness (toggling back and forth, searching, revisiting pages). Of the 73 videos, two analysts independently reviewed 55 (75%) of the videos and made a binary decision whether the participant was lost or not lost (similar to the characterization described by Smith).

Lost Example: For example, one participant, when looking for the US Bank General Counsel, kept going back to the home page, scrolling to the bottom of the page multiple times, and using the search bar multiple times. This participant’s lostness score was .64 and was marked as “lost” by the evaluator.

Not Lost Example: In contrast, another participant, when looking for checking account fees, clicked a checking account tab, inputted their zip code, found the fees, and stopped the task. This participant’s lostness score was 0 (perfect) and was marked as “not lost” by the evaluator.

Table 5 shows the number of participants identified as lost by the evaluators corresponding to their lostness score grouped into deciles.

Lostness Score N # Lost % Lost
0 28 1 4%
0.3 6 2 33%
0.4 1 1 100%
0.5 3 1 33%
0.6 4 4 100%
0.7 5 5 100%
0.8 8 6 75%

Table 5: Percent of participants characterized as lost or not lost from evaluators watching the videos.

For example, of the 28 participant videos with a lostness score of 0, only 1 (4%) was considered lost. In contrast, 6 out of the 8 (75%) participants with lostness scores between .8 and .9 were considered lost. We do see good corroboration with the Smith thresholds. Only 9% (3 of 34) of participants with scores below .4 were considered lost. Similarly, 89% (16 of 18) participants were considered lost who had scores above .5.

Another way to look at the data, participants who were lost had a lostness score that was more than 5 times as high as those who weren’t lost (.61 vs .11; p <.01).

 

Summary and Takeaways

An examination of a method for measuring lostness revealed:

Lostness as path taken relative to the happy path. An objective lostness measure was proposed over 20 years ago that uses the sum of two ratios: the number of unique pages relative to the minimum number of pages, and the total number of pages relative to the unique number of pages. Computing this lostness measure requires identifying the minimum number of pages or steps needed to complete a task (the happy path) as well as counting all screens and the number of unique screens (a time-consuming process). A score of 0 represents perfectly efficient navigation (not lost) while a score of 1 indicates being very lost.

Thresholds are supported but not meant for task failure. Data from the original validation study had suggested lostness values below .4 indicated that participants weren’t lost and values above .5 as participants being lost. Our data corroborated these thresholds as 91% of participants with scores below .4 were not considered lost and 89% of participants with scores above .5 were lost. The thresholds and score, however, become less meaningful when a user fails or abandons a task and visits only a subset of the essential screens, which decreases their lostness score. This suggests lostness may be best as a secondary measure to other usability metrics, notably task completion.

Perceived ease explains lostness. In our data, we found that average task-ease scores (participant ratings on the 7-point SEQ) explained 95% of the variance in lostness scores. At least with our data, in general, when participants were lost, they knew it and rated the task harder (at least when aggregated across tasks). While subjective measures aren’t a substitute for objective measures, they do correlate, and post-task ease is quick to ask and analyze. Lower SEQ scores already indicate a need to look further for the problems and this data suggests participants getting lost may be a culprit for some tasks.

Time-consuming process is ideally automated. To collect the data for this validation study we had to review participant videos several times to compute the lostness score (counting screens and unique screens). It may not be worth the effort to review videos just to identify a lostness score (especially if you’re able to more quickly identify the problems users are having with a different measure). However, a lostness score can be computed using software (something we are including in our MUIQ platform). Researchers will still need to input the minimal number of steps (i.e., the happy path) per task but this measure, like other measures such as clicking non-clickable elements, may help quickly diagnose problem spots.

There’s a distinction between browsing and being lost. The tasks used in our replication study all had specific answers (e.g. finding a checking account’s fees). These are not the sort of tasks participants likely want to spend any more time (or steps) on than they need to. For these “productivity” tasks where users know exactly what they need to do or find, lostness may be a good measure (especially if it’s automatically collected). However, for more exploratory tasks where only a category is defined and not a specific item, like browsing for clothing, electronics, or the next book to purchase, the natural back-and-forth of browsing behavior may quantitatively look like lostness. A future study can examine how well lostness holds up under these more exploratory tasks.

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Source: Validating a Lostness Measure

Jul 18, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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CS109 Data Science

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Storytelling with Data: A Data Visualization Guide for Business Professionals

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Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:What is POC (proof of concept)?
A: * A realization of a certain method to demonstrate its feasibility
* In engineering: a rough prototype of a new idea is often constructed as a proof of concept

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[ VIDEO OF THE WEEK]

Ashok Srivastava(@aerotrekker @intuit) on Winning the Art of #DataScience #FutureOfData #Podcast

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Data beats emotions. – Sean Rad, founder of Ad.ly

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#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

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Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

HP boosts Vertica big data capabilities with streaming analytics

HP chases big data strategy with Vertica additions and startup accelerator

HP has revealed an updated version of its Vertica big data analytics platform in a bid to fulfil a data-oriented strategy that benefits businesses and non-data scientists.

HP Vertica will gain data streaming capabilities and advanced log file text searching to enable high-speed analytics on big data collected from sources such as the Internet of Things (IoT).

The new version of Vertica, codenamed Excavator, will offer support for Apache Kafka, an open source distributed messaging system, to allow organisations to harvest and analyse streaming data in near real time.

HP claimed that this new capability allows Excavator to be used in a wide range of monitoring and process control deployments in sectors such as manufacturing, healthcare and finance.

The addition of advanced machine log text search in Excavator will allow companies to collect and organise large log file datasets generated by systems and applications and provide more scope in predicting and identifying application failures and cyber attacks, along with the ability to see authorised and unauthorised access to apps.

HP showed its commitment to big data-driven businesses by announcing the Haven Startup Accelerator, a programme designed to expand HP’s ecosystem of developers by offering free access to community versions of Vertica, and affordable access to the firm’s big data software and services.

Embracing open source
HP has added native integration of Vertica with Apache Spark in a move to embrace the scalability of open source software and platforms for big data analytics. The firm has also enabled Vertica to support SQL queries made on native files and popular formats found in Hadoop data and deployments.

HP will integrate Vertica with Apache Spark to allow data to be transferred between the database platform and the cluster computing framework, giving developers the option to build data models in Spark and run them through Vertica’s analytics capabilities.

Furthermore, the company is making its Flex Zone available as open source software, which allows companies to analyse semi-structured data without needing to carry out intensive coding to prepare a system for the data ahead of analysis.

HP appears to be bolstering its portfolio of enterprise-grade products in preparation for its split into two separate companies, Hewlett Packard Enterprise and HP Inc.

Note: This article originally appeared in V3. Click for link here.

Source

The Importance of Workforce Analytics

Although organizations must make decisions based on a variety of factors and perspectives, few are as important as human resources when it comes to taking action. A company’s workforce is vitally important but concurrently one of the more complex sides of operating a business. Instead of clean, hard data, employees can present a variety of qualitative factors that are hard to put into numbers that work for analytics.

Even so, an organization’s human capital is perhaps its most important asset. Building an in-depth understanding of your staff can, therefore, deliver better answers and give you a competitive edge. More than acting as a way of punishing employees, however, workforce analytics—sometimes called people analytics—can empower your team by providing better insights as to what works and doesn’t. Furthermore, it can help uncover all the tools employees need to succeed. Let’s begin by breaking down the meaning of workforce analytics.

What are Workforce Analytics?

Workforce analytics, which is a part of HR analytics, are used to track and measure employee-related data and optimize organizations’ human resource management and decision-making. The field focuses on much more than hiring and firing by also concentrating on the return on value for every hire. Moreover, it highlights more specific data that assists with identifying workplace trends such as potential risk factors, satisfaction with decisions, and more.

Additionally, workforce analytics can evaluate more than just existing staff by also analyzing the trends that surround employment. For instance, companies can see which periods of the year have a higher number of applicants and adjust their recruitment efforts, or measure diversity efforts as well as employee engagement without having to resort to more invasive or subjective methods that may provide false positives.

What Are some Key Benefits of Workforce Analytics?

More so than tracking the number of employees and what they’re making, workforce analytics provides a comprehensive view of your organization’s workers designed to interpret historic trends and create predictive models that lead to insights and better decisions in the future. Some of the key benefits of workforce analytics include:

  • Find areas where efficiency can be improved with automation – While workers are an asset to a company, sometimes the tasks they do can reduce their productivity or provide minimal returns. Workforce analytics can discover areas where tasks can be relegated to machines via automation, allowing workers to instead dedicate their efforts to more important and valuable activities.
  • Improve workers’ engagement by understanding their needs and satisfaction – More than simply looking for firing and hiring information, workforce and people analytics can help a company understand why their employees are not performing their best, and the factors that are impacting productivity. This is more to maintain the current workforce instead of replacing it. The goal is to uncover those factors affecting performance and engagement and to overcome them by fostering better conditions.
  • Create better criteria for hiring new staff and provide a better hiring process – Finding new talent is always complex regardless of a company’s size or scope. Workforce analytics can shed light exactly on what is needed from a new hire by a department based on previous applicants, their success, and the company’s needs. More importantly, they can understand new candidates based on this historical data to determine whether they would be a good fit or not. For instance, a company seeking to hire a new developer may think twice about hiring a server-side programmer after several previous hires with similar experience didn’t work out.

What Key Metrics Should I Track for Workforce Analytics?

  • Employee productivity – We still talk about the 9 to 5 work day, but the current reality for many employees dictates that work hours tend to be more flexible and variable. As such, measuring productivity by the number of hours worked is no longer fully accurate. Instead, creating a productivity index which includes a few different data points will give a much better idea of how employees are performing.
  • Early turnover – Another important area that is often neglected when measuring satisfaction is how quickly employees are leaving on their own. A high early turnover rate is an indicator that things are not working both in terms of meeting expectations and employee satisfaction.
  • Engagement – This may seem superfluous, but employees who are engaged with their work are more likely to be productive. Measuring engagement includes tracking employee satisfaction, stress levels, and employees’ belief in the company’s ideals. High engagement is a great sign that HR is doing its job.

Conclusion

Focusing your data gathering internally can help you improve your company’s productivity. By honing in on your human resources and finding ways to empower your team, people analytics can boost your company’s efficiency, leading to happier and more productive colleagues.

Source

Jul 11, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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>> The Challenges Canadian Companies Face When Implementing Big Data by analyticsweekpick

>> Voices in AI – Episode 89: A Conversation with Doug Lenat by analyticsweekpick

>> BARC Survey Shows New Benefits from Embedded Analytics by analyticsweek

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Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

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The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

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People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

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Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

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[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

Understanding #FutureOfData in #Health & #Medicine - @thedataguru / @InovaHealth #FutureOfData #Podcast

 Understanding #FutureOfData in #Health & #Medicine – @thedataguru / @InovaHealth #FutureOfData #Podcast

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[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter

Webinar: Improving the Customer Experience Using Big Data, Customer-Centric Measurement and Analytics

pivotal-logo-taglineI recently gave a talk on how to improve the customer experience using Big Data, customer-centric measurement and analytics. My talk was hosted by the good people at Pivotal (recently Cetas).

You can view the webinar by registering here or you can view the slides below. In this webinar, Improving the Customer Experience Using Big Data, Customer-Centric Measurement and Analytics, I include content from my new book “TCE – Total Customer Experience: Building Business Through Customer-Centric Measurement and Analytics.” I discuss three areas: measuring the right customer metrics, integrating disparate data silos and using Big Data to answer strategic business questions. Using the right customer metrics in conjunction with other business data, businesses will be able to extract meaningful results that help executives make the right decisions to move their company forward.

In the book, I present best practices in measurement and analytics for customer experience management (CEM) programs.  Drawing on decades of research and practice, I illustrate analytical best practices in the field of customer experience management that will help you increase the value of all your business data to help improve the customer experience and increase customer loyalty.

 

Originally Posted at: Webinar: Improving the Customer Experience Using Big Data, Customer-Centric Measurement and Analytics by bobehayes

Jul 04, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ FEATURED COURSE]

Master Statistics with R

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In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… more

[ FEATURED READ]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
A: * Effect would be similar to regularization: avoid overfitting
* Used to increase robustness

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[ VIDEO OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

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[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

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[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter

How Google Is Using People Analytics To Completely Reinvent HR

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First of two parts

If you haven’t seen it in the news, after its stock price broke the $800 barrier last month, Google moved into the No. 3 position among the most valuable firms in the world.

Google is clearly the youngest firm among the leaders; it has surprisingly been less than a decade since Google’s IPO.

Most companies on the top 20 market cap list could be accurately described as “old school,” because most can attribute their success to being nearly half a century old, having a long established product brand, or through great acquisitions. Google’s market success can instead be attributed to what can only be labeled as extraordinary people management practices that result from its use of “people analytics.”

A new kind of people management

The extraordinary marketplace success of Google (and Apple, which is No. 1 on the list) is beginning to force many business leaders to take notice and to come to the realization that there is now a new path to corporate greatness.

“New path” firms dominate by producing continuous innovation. And executives are beginning to learn that continuous innovation cannot occur until a firm makes a strategic shift toward a focus on great people management.

A strategic focus on people management is necessary because innovations come from people, and you simply can’t maximize innovations unless you are capable of recruiting and retaining innovators. And even then, you must provide them with great managers and an environment that supports innovation.

Unfortunately, making that transition to an innovative firm is problematic because almost every current HR function operates under 20th century principles of past practices, efficiency, risk avoidance, legal compliance, and hunch-based people management decisions. If you want serial innovation, you will need to reinvent traditional HR and the processes that drive innovation.

Shifting to data-based people management

The basic premise of the “people analytics” approach is that accurate people management decisions are the most important and impactful decisions that a firm can make. You simply can’t produce superior business results unless your managers are making accurate people management decisions.

Many do argue that product R&D, marketing, or resource allocation decisions are instead the most impactful decisions. However, each one of those business decisions is made by an employee. If you hire and retain mostly mediocre people and you provide them with little data, you can only assume that they will make mediocre decisions in each of these important business areas, as well as in people management decisions.

No one in finance, supply chain, marketing, etc. would ever propose a solution in their area without a plethora of charts, graphs, and data to support it, but HR is known to all too frequently rely instead on trust and relationships. People costs often approach 60 percent of corporate variable costs, so it makes sense to manage such a large cost item analytically.

Another major problem in HR is its traditional reliance on relationships. Relationships are the antithesis of analytical decision-making. The decision-making “currency” for most business decisions has long been data, but up until now, HR has relied on a different currency: that of building relationships.

In direct contrast, Google’s success has to be attributed in large part to the fact that it is the world’s only data-driven HR function. Google’s business success should convince executives at any firm that wants to grow dramatically that they must at least consider adopting the data and analytically based model used by Google. Its approach has resulted in Google producing amazing workforce productivity results that few can match (on average, each employee generates nearly $1 million in revenue and $200,000 in profit each year).

How does the Google approach reinvent HR?

HR at Google is dramatically different from the hundreds of other HR functions that I have researched and worked with. To start with, at Google it’s not called human resources; instead, the function is called “people operations.” The VP and HR leader Laszlo Bock has justifiably learned to demand data-based decisions everywhere.

People management decisions at Google are guided by the powerful “people analytics team.” Two key quotes from the team highlight their goals:

  • “All people decisions at Google are based on data and analytics.”
  • The goal is to … “bring the same level of rigor to people-decisions that we do to engineering decisions.”

Google is replacing the 20th century subjective decision-making approach in HR. Although it calls its approach “people analytics,” it can alternatively be called “data-based decision-making,” “algorithm based decision-making,” or “fact or evidence-based decision-making.”

Top 10 reasons for Google’s people analytics approach

The people analytics team reports directly to the VP and it has a representative in each major HR function. It produces many products, including employee surveys that are not anonymous, and dashboards. It also attempts to identify insightful correlations and to provide recommended actions. The goal is to substitute data and metrics for the use of opinions.

Almost everyone has by now heard about Google’s free food, 20% time, and wide range of fun activities but realize that each of these was implemented and are maintained based on data. Many of Google’s people analytics approaches are so unusual and powerful, I can only describe them as “breathtaking.”

Below I have listed my “Top 10” of Google’s past and current people management practices to highlight its data-driven approach:

  1. Leadership characteristics and the role of managers –ts “project oxygen” research analyzed reams of internal data and determined that great managers are essential for top performance and retention. It further identified the eight characteristics of great leaders. The data proved that rather than superior technical knowledge, periodic one-on-one coaching which included expressing interest in the employee and frequent personalized feedback ranked as the No. 1 key to being a successful leader. Managers are rated twice a year by their employees on their performance on the eight factors.
  2. The PiLab — Google’s PiLab is a unique subgroup that no other firm has. It conducts applied experiments within Google to determine the most effective approaches for managing people and maintaining a productive environment (including the type of reward that makes employees the happiest). The lab even improved employee health by reducing the calorie intake of its employees at their eating facilities by relying on scientific data and experiments (by simply reducing the size of the plates).
  3. A retention algorithm — Google developed a mathematical algorithm to proactively and successfully predict which employees are most likely to become a retention problem. This approach allows management to act before it’s too late and it further allows retention solutions to be personalized.
  4. Predictive modeling – People management is forward looking at Google. As a result, it develops predictive models and use “what if” analysis to continually improve their forecasts of upcoming people management problems and opportunities. It also uses analytics to produce more effective workforce planning, which is essential in a rapidly growing and changing firm.
  5. Improving diversity – Unlike most firms, analytics are used at Google to solve diversity problems. As a result, the people analytics team conducted analysis to identify the root causes of weak diversity recruiting, retention, and promotions (especially among women engineers). The results that it produced in hiring, retention, and promotion were dramatic and measurable.
  6. An effective hiring algorithm – One of the few firms to approach recruiting scientifically, Google developed an algorithm for predicting which candidates had the highest probability of succeeding after they are hired. Its research also determined that little value was added beyond four interviews, dramatically shortening time to hire. Google is also unique in its strategic approach to hiring because its hiring decisions are made by a group in order to prevent individual hiring managers from hiring people for their own short-term needs. Under “Project Janus,” it developed an algorithm for each large job family that analyzed rejected resumes to identify any top candidates who they might have missed. They found that they had only a 1.5% miss rate, and as a result they hired some of the revisited candidates.
  7. Calculating the value of top performers – Google executives have calculated the performance differential between an exceptional technologist and an average one (as much as 300 times higher). Proving the value of top performers convinces executives to provide the resources necessary to hire, retain, and develop extraordinary talent. Google’s best-kept secret is that people operations professionals make the best “business case” of any firm in any industry, which is the primary reason why they receive such extraordinary executive support.
  8. Workplace design drives collaboration – Google has an extraordinary focus on increasing collaboration between employees from different functions. It has found that increased innovation comes from a combination of three factors: discovery (i.e. learning), collaboration, and fun. It consciously designs its workplaces to maximize learning, fun, and collaboration (it even tracks the time spent by employees in the café lines to maximize collaboration). Managing “fun” may seem superfluous to some, but the data indicates that it is a major factor in attraction, retention, and collaboration.
  9. Increasing discovery and learning – Rather than focusing on traditional classroom learning, the emphasis is on hands-on learning (the vast majority of people learn through on the job learning). Google has increased discovery and learning through project rotations, learning from failures, and even through inviting people like Al Gore and Lady Gaga to speak to their employees. Clearly self-directed continuous learning and the ability to adapt are key employee competencies at Google.
  10. It doesn’t dictate; it convinces with data — The final key to Google’s people analytics team’s success occurs not during the analysis phase, but instead when it present its final proposals to executives and managers. Rather than demanding or forcing managers to accept its approach, it instead acts as internal consultants and influences people to change based on the powerful data and the action recommendations that they present. Because its  audiences are highly analytical (as most executives are), it uses data to change preset opinions and to influence.

Article originally published Here.

Originally Posted at: How Google Is Using People Analytics To Completely Reinvent HR

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData

[youtube https://www.youtube.com/watch?v=wpyobHjuybM]

In this podcast Steve Goldbach & Geoff Tuff from Deloitte sat with Vishal to discuss their recently release book “Detonate”. They shared their insights on a cleaner way to create strategies for a future proof and transformation friendly organization. Their tactical suggestions goes a long way in helping install a robust strategy to increase responsiveness.

Steve / Geoff’s Recommended Read:
Geoff’s suggestion:
Cloud Atlas: A Novel by David Mitchell The Opposable mind https://amzn.to/2rA5BAV
The Last Days of Night: A Novel by Graham Moore https://amzn.to/2rAvErB

Steve’s suggestion:
Thinking, Fast and Slow by Daniel Kahneman https://amzn.to/2ryIx5C
The Opposable Mind: How Successful Leaders Win Through Integrative Thinking by Roger L. Martin https://amzn.to/2Kds7Y2
The Big Short: Inside the Doomsday Machine by Michael Lewis https://amzn.to/2KaFgRI

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Geoff’s Bio:
GEOFF TUFF is a principal at Deloitte and a senior leader of the firm’s Innovation and Applied Design practices. In the past, he led the design firm Doblin and was a senior partner at Monitor Group, serving as a member of its global Board of Directors before the company was acquired by Deloitte. He has been with some form of Monitor for more than 25 years. He holds degrees from Dartmouth College and Harvard Business School.

Steve’s Bio:
STEVEN GOLDBACH is a principal at Deloitte and serves as the organization’s chief strategy officer. He is also a member of the Deloitte U.S. executive leadership team. Before joining Deloitte, Goldbach was a partner at Monitor Group and head of its New York office. Goldbach helps executives and their teams transform their organizations by making challenging and pragmatic strategy choices in the face of uncertainty. He is an architect, expert practitioner, and teacher of the variety of strategy methodologies developed and used by Monitor Deloitte over the years. Serving clients in many industries, including consumer products, telecommunications, media and health care, Goldbach helps companies combine rigor and creativity to create their own future. He holds degrees from Queen’s University at Kingston and Columbia Business School

About #Podcast:
#FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

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Keywords:
#FutureOfData #Leadership #Podcast #Future of #Work #Worker & #Workplace

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